Comparison of methods for the assessment of nonlinearity in short-term heart rate variability under different physiopathological states

Abstract

Despite the widespread diffusion of nonlinear methods for heart rate variability (HRV) analysis, the presence and the extent to which nonlinear dynamics contribute to short-term HRV is still controversial. This work aims at testing the hypothesis that different types of nonlinearity can be observed in HRV depending on the method adopted and on the physiopathological state. Two entropy-based measures of time series complexity (normalized complexity index, NCI) and regularity (information storage, IS), and a measure quantifying deviations from linear correlations in a time series (Gaussian linear contrast, GLC), are applied to short HRV recordings obtained in young (Y) and old (O) healthy subjects and in myocardial infarction (MI) patients monitored in the resting supine position and in the upright position reached through head-up tilt. The method of surrogate data is employed to detect the presence of and quantify the contribution of nonlinear dynamics to HRV. We find that the three measures differ both in their variations across groups and conditions and in the number and strength of nonlinear HRV dynamics detected: at rest, IS reveals a significantly lower number of nonlinear dynamics in Y, whereas during tilt GLC reveals significantly stronger nonlinear HRV dynamics in MI; in the transition from rest to tilt, all measures detect a significant weakening of nonlinear HRV dynamics in Y, while only GLC detects a significant strengthening of such dynamics in MI. These results suggest that distinct dynamic structures, detected with different sensitivity by nonlinear measures, lie beneath short-term HRV in different physiological states and pathological conditions.

Multivariate and Multiscale Complexity of Long-Range Correlated Cardiovascular and Respiratory Variability Series

“…The complexity of HP assessed at lower time scales (cutoff 0.4 Hz) always decreases in the UP position, for all measures (univariate, both bivariate, and multivariate), as shown in Figure 4 . This documents the well-known simplification of heart rate variability induced by head-up tilt, which is known to evoke sympathetic activation and vagal withdrawal making the cardiac dynamics more regular [ 31 , 40 , 43 , 44 ]. The fact that this result is observed identically for the eVAR and eVARFI approaches indicates that long-range correlations do not impact significantly the evaluation of complexity performed at short time scales.…”

Entropy Analysis of RR-Time Series From Stress Tests

“…Finally, we mention possible limitations of the present study: it is known that gender has a substantial effect on HRV. In a future work the size of the database will be increased to be able to make precise gender and age distinctions, because these influences need to be considered when performing HRV studies even if these influences only partly differ (Voss et al, 2015;Faes et al, 2019). It is also necessary to complement the study with the application of other non-linear techniques.…”

“…Therefore at rest when both levels are active, the fluctuations are greater compared to exercise when there is a single level of attraction (see Figure 3C). As the HRV time series are complex, physiological interpretations based on entropy measures should be provided with caution (Xiong et al, 2017;Faes et al, 2019). As Xiong et al (2017) have stated, entropy measurements can only differentiate changes of specific types in cardiac dynamics and that proper preprocessing is vital for correct estimation and interpretation.…”

“…Such fluctuations have been represented as a superimposition of rhythms, which contribute to the neuroautonomic modulation of the heart rhythm in healthy conditions, and are altered by a wide variety of disease states. In fact, there is a consensus among the scientific community that the long-term RR interval time series are non-linear and multifractal and that the HRV scale behavior is altered with aging, during physical exercise and in pathological conditions such as for example, myocardial infarction (Iyengar et al, 1996;Ivanov et al, 1999a;Huikuri et al, 2000;Bernaola-Galván et al, 2017;Gómez-Extremera et al, 2018;Faes et al, 2019). It is also widely accepted that the evaluation of HRV on different time scales has allowed us to give a quite satisfactory explanation of the short-term mechanisms underlying cardiovascular control (Malliani et al, 1991;Cohen and Taylor, 2002;Xiong et al, 2017).…”

Information Decomposition in the Frequency Domain: a New Framework to Study Cardiovascular and Cardiorespiratory Oscillations

“…The potentiality of combining information and spectral decompositions was verified experimentally in the proposed application to cardiovascular and cardiorespiratory interactions. Indeed, although significant modifications of physiological dynamics induced by the transition from rest to tilt are detectable using simpler univariate variability markers (see, e.g., [12,37,41,42]), the proposed multivariate indexes can describe peculiar features that might be correlated with specific properties of the physiological dynamical systems that go beyond their traditional assessment based on power of oscillations, transfer function gain and latency but deal with higher level functions related to the overall organization of the cardiovascular control system like synergy and redundancy. Specific results observed in this work are the opposite response to tilt observed integrating the cardiovascular interaction information within the LF and HF frequency bands, with the increase in the LF band and the decrease in HF band associated respectively to sympathetic activation and parasympathetic withdrawal, and the different trends observed with tilt for the global and unique measures of coupling between HP ad SAP, with tendency to decrease for and to increase for that highlight the dampening of redundant interactions in the response to postural stress.…”